Fault prognosis is important in process systems since it can predict potential faults in industrial equip-ment in a timely manner and hence reduce the occurrence of accidents and economic losses.Due to the complexity of process systems and the uneven distribution of data sets,the conventional method of using the normal data set to predict the operating state offline is not versatile and inaccurate.In response to the above problems,this paper combines a convolutional neural network(CNN)with a long-short term memory network(LSTM)to extract the characteristics of boiler operating data and predict the operating state after online prediction.In the local outlier fac-tor(LOF)model,the outliers of the time series are calculated and predicted.The results are compared with the fault threshold trained in the offline state,and if it is greater than the threshold,it is considered that there is a po-tential risk.The model was used in the Tennessee-Eastman(TE)process,and compared with traditional fault prognosis methods.The results show that the model performs well in multi-fault and single-fault prognosis,and out-liers could be detected earlier by one sampling window.The results indicate the model has potential applications in fault prognosis in industrial process systems.
关键词
故障预测/田纳西-伊斯曼过程/长短期记忆/局部异常因子算法/卷积神经网络
Key words
fault prognosis/Tennessee-Eastman process/long-short term memory/local outlier factor/convolu-tional neural network
维普
万方数据